Method of and apparatus for classifying an image

ABSTRACT

A technique is provided for classifying images in a library ( 1 ) for subsequent selective retrieval. Each library image is segmented ( 2 ) into a plurality of coherent regions. Each of the regions is analyzed in respect of a plurality of visual properties, each of which is ascribed a number according to the value of the visual property. The property numbers are then compared ( 4 ) with bands of the available property range and a computer-readable character, such as a letter of the alphabet is ascribed to each of the property values. The characters are then arranged ( 5 ) in a predetermined order to form a region character string. The strings for each region are then indexed ( 7 ) to form an index ( 8 ) of the images by region character string. For image retrieval, a target image ( 10 ) is analyzed in the same way to form region character strings which are then supplied to a text retrieval system ( 12 ) for detecting matches with entries in the index ( 8 ). Library images having matching regions are retrieved from the library ( 1 ).

The present invention relates to a method of and an apparatus forclassifying an image. The present invention also relates to an index ofimages, a method of and an apparatus for image retrieval, a program anda medium containing such a program.

A known type of image retrieval system is based on generating andsearching global image properties such as colour histograms or texturefilter banks. However, such techniques are inadequate for retrieval fromlarge collections of diverse images. Image retrieval techniques based onlocalised image properties are known and are generally based on eitherof two image-partitioning schemes. In the first scheme, the image issplit into easily defined regular sub-divisions to which global measuresare applied. An example of this technique is disclosed in Squire, D. M.,Muller, H., and Raki, J. “Content-based query of image databases,inspirations from text retrieval: inverted files, frequency-basedweights and relevance feedback”. Proceedings of SCIA99, 1999. In thesecond scheme, the image is segmented into regions based on edges,colour and texture using the properties of the regions to build an indexof an image collection. A technique of this type is disclosed in Howe,N. R. “Percentile blobs for image similarity.” IEEE Workshop onContent-based Access to Image and Video Libraries, pp 78–83, 1998 andWood, M. E. J., Campbell, N. W. and Thomas, B. T. “Interactiverefinement by relevance feedback in content-based digital imageretrieval” Proceedings of ACM Multimedia 98, pp 13–20, 1998. Bothtechniques generally use multi-dimensional indexing or graph-matchingtechniques for image retrieval, although Squire et al (see above) adapta text-based information retrieval technique to the above-mentionedfirst image-partitioning scheme.

According to a first aspect of the invention, there is provided a methodof classifying an image, comprising the steps of segmenting the imageinto a plurality of regions and, for at least one of the regions:

-   -   quantifying each of a plurality of visual properties of the        region on a numerical scale for the property;    -   comparing each quantified property with a plurality of bands of        the numeric scale for the property, each band being associated        with a computer-readable character; and    -   arranging in a predetermined order the characters associated        with the bands in which the quantified properties fall to form a        region character string.

The segmenting step may comprise segmenting the image into a pluralityof coherent regions.

The visual properties may include area. The numeric scale for area maybe logarithmic.

The visual properties may include at least one colour property. Thenumeric scale for the colour property may be linear.

The visual properties may include brightness. The numeric scale forbrightness may be linear.

The visual properties may include shape.

The visual properties may include texture.

Each region character string may include two-dimensional coordinatesrepresenting the position of the corresponding region in the image.

The method may comprise supplying at least one of the quantifiedproperties to at least one neural network which is trained to recogniseat least one substance and forming, in response to recognition by theneural network, another region character string. The other regioncharacter string may be descriptive of the at least one substance. Theother region character string may be a semantic word.

The method may comprise associating the region character strings withthe image.

The region character string may be embedded in a markup language.

According to a second aspect of the invention, there is provided amethod of classifying a plurality of images, comprising classifying eachof the images by a method according to the first aspect of theinvention.

The method may comprise forming an index of the images by the regioncharacter strings.

According to a third aspect of the invention, there is provided an indexformed by such a method.

According to a fourth aspect of the invention, there is provided amethod of image retrieval, comprising generating at least one regioncharacter string and comparing the at least one character string withthe character strings formed by a method according to the second aspectof the invention.

The comparison may be performed by a text retrieval system.

The at least one region character string may be generated by a methodaccording to the first aspect of the invention.

A match may be indicated when at least some of the characters of the atleast one generated character string match with the correspondingcharacters of the character strings formed by a method according to thesecond aspect of the invention.

The method may comprise generating at least one further region characterstring from the at least one generated region character string bychanging at least one of the characters to a character associated with aband adjacent the band in which the corresponding quantified propertyfalls and comparing the at least one further region character stringwith the character strings formed by a method according to the secondaspect of the invention.

According to a fifth aspect of the invention, there is provided a methodof organising a plurality of images, comprising classifying the imagesby a method according to the second aspect of the invention andarranging the images in accordance with the region character stringsassociated therewith.

Images whose region character strings match most closely may be arrangedadjacent each other.

According to a sixth aspect of the invention, there is provided anapparatus for performing a method according to the first, second, fourthor fifth aspect of the invention.

The apparatus may comprise a programmed computer.

According to a seventh aspect of the invention, there is provided aprogram for controlling a computer of such an apparatus.

According to an eighth aspect of the invention, there is provided amedium containing a program according to the sixth aspect of theinvention.

According to a ninth aspect of the invention, there is provided anapparatus for classifying an image, comprising:

-   -   means for segmenting the image into a plurality of regions;    -   means for quantifying each of a plurality of visual properties        of at least one of the regions on a numeric scale for the        property;    -   means for comparing each quantified property with a plurality of        bands of the numeric scale for the property, each band being        associated with a computer readable character; and    -   means for arranging in a predetermined order the characters        associated with the bands in which the quantified properties        fall to form a region character string.

It is thus possible to provide techniques for classifying images in away such as to permit effective searching and image retrieval. By usingcomputer-readable characters arranged in strings to classify thecoherent regions of each image, text-based information retrieval systemsmay be used for image retrieval. Such systems provide efficientretrieval of images on the basis of image content. Another advantage ofthis technique is that it allows descriptions of image content to beembedded in markup languages such as XML.

The invention will be further described, by way of example, withreference to the accompanying drawings, in which:

FIG. 1 is a block schematic diagram of a system for classifying imagesconstituting a first embodiment of the invention;

FIG. 2 is a block schematic diagram of a system for retrieving imagesconstituting a second embodiment of the invention;

FIG. 3 shows a photograph of an image to be classified;

FIG. 4 illustrates regions resulting from segmentation of the image ofFIG. 3;

FIG. 5 shows a photograph of another image to be classified;

FIG. 6 illustrates regions resulting from segmentation of the image ofFIG. 5;

FIG. 7 shows a photograph of an image for entry to the retrieval systemshown in FIG. 2;

FIG. 8 illustrates regions resulting from segmentation of the image ofFIG. 7; and

FIG. 9 is a block schematic diagram of an apparatus for embodying thesystems of FIGS. 1 and 2.

FIG. 1 illustrates a system for classifying images contained in an imagelibrary 1. The image library 1 contains a plurality of images incomputer-readable form, for example digitally encoded and stored in asuitable storage medium such as CD-ROM or accessible from remote storagesuch as via the internet. Although it is possible to classify individualimages in real time, for example captured by a digital camera, it isgenerally preferable for images to be stored after capture and thenpresented to the system for classification.

The images are supplied one at a time to an image segmenter 2 whichsegments the image into a plurality of coherent regions. In thiscontext, a coherent region is an area of the image of generally uniformcolour and/or texture whose boundaries occur where there is asignificant change in colour and/or texture. The actual technique usedfor image segmentation is not important but should be such as to providegood quality segmentation. For example, there are various knowntechniques for segmenting images and one such technique makes use ofcolour edge detection and Voronoi-seeded region growing, for example asdisclosed in Sinclair, D. “Voronoi seeded colour image segmentation”,Technical Report 1999.3, AT&T Laboratories Cambridge,http://www.uk.research.att.com/abstracts.html. and in Sinclair, D. andWood, K. R. “Colour edge detection and Voronoi-seeded segmentation forregion-based image retrieval”. Submitted to the International Conferenceon Computer Vision (ICCV99). 8 pp, 1999 and in the references referredto in this paper, the subject matter of all of which papers isincorporated herein by reference.

The image segmenter 2 supplies each region for analysis to a regionanalyser 3. The region analyser 3 analyses each region in turn inrespect of colour distribution, texture patterns and shape. For example,the colour distribution may be analysed so as to assess the mean colourof the region and the colour covariance and a colour histogram may beformed. The shape analysis may include analysing the boundary shape,determining the absolute size of the region, determining the size of theregion relative to the size of the whole image, and position of theregion relative to the boundaries of the image. An example of atechnique which may be used as or as part of such shape analysisinvolves computing, for each picture element (pixel) on the boundary ofeach region, the local curvature and the width of the regionperpendicular to the local curvature. The set of pairs of curvatureagainst width forms a representation of the region shape and this datamay be clustered or loaded into a histogram. However, any suitabletechnique may be used and other examples are disclosed in:

-   -   A. Ashbrook, N. Thacker, and P. Rockett. “Pairwise geometric        histograms: A scaleable solution for the recognition of 2d rigid        shape.” Technical Report 94/30. Sheffield University, Electronic        Systems Group, 1994; I. Biederman. “Matching image edges to        object memory.” Int. Conf. on Computer vision, pages 384–392,        1987; D. Forsyth, J. Malik, M. Fleck, and J. Ponce. “Primitives,        perceptual organisation and object recognition.” Technical        Report http://HTTP.CS.Berkeley.EDU/daf/vrll.ps.Z, University of        California, Berkeley, Computer Science Division, 1997; B. Huet        and E. Hancock. “Fuzzy relational distance for large-scale        object recognition.” Proc. Conf. Computer Vision and Pattern        Recognition, pages 138–143, 1998; R. Rangayyan, M.        El-Faramawy, J. Desautels, and O. Alim. “Measure of acutance and        shape for classification of breast tumours.” IEEE Transactions        on Pattern Analysis and Machine Intelligence, 16:799–810,        1997; E. Rivlin, S. Dickinson, and A. Rosenfeld. “Recognition by        functional parts.” Proc. Conf. Computer Vision and Pattern        Recognition, pages 267–274, 1994; C. A. Rothwell, A. P.        Zisserman, J. L. Mundy, and D. A. Forsyth. “Efficient model        library access by projectively invariant indexing functions.”        Proc. Conf. on Computer Vision and Pattern Recognition, 1992; H.        Tek, P. Stoll, and B. Kimia “Shocks from images: propagation of        orientation elements.” Proc. Conf. Computer Vision and Pattern        Recognition, pages 839 845, 1997; and S. Sclaroff and A.        Pentland, “Modal matching for correspondence and recognition.”        IEEE Trans. Pattern Analysis Mach. Intell. 17 pages 545–561        (1995), the contents of all of which are incorporated herein by        reference.

Examples of texture analysis are disclosed in S. Zhu, Y. Wu and D.Mumford, “Minimax entropy principle and its application to texturemodelling”, Neural Computation, Vol. 9, pp 1627–1660, 1997 and T.Hofman, J. Puzicha and J. Buhman, “Unsupervised texture segmentation ina deterministic annealing framework”, IEEE Transactions on PatternAnalysis and Machine Intelligence, 20 (8), pp 803–818, 1998, thecontents of which are incorporated herein by reference.

For each of at least some of the region properties which are analysed bythe analyser 3, the property may have any numerical value within a rangeof possible values. The range of values of each such property ispartitioned into contiguous bands or sub-ranges and each such band isassigned a computer-representable or computer-readable character. Theregion analyser 3 then assigns, for each region of each image, acharacter for the band in which the value of each of its propertiesfalls. A string assembler 5 receives the characters for each region ofeach image and assembles them in a predetermined order corresponding toa predetermined order of the properties so as to form a region characterstring which represents the appearance of the coherent region. Forexample, the computer-readable characters may be letters of thealphabet, such as lower case letters. An example of visual properties,letter ranges and scale for mapping region properties to letters is asfollows:

Property Letter Range Scale Area a–z logarithmic Colour (red) a–f linearColour (green) a–f linear Colour (blue) a–f linear Sharp (entropy) a–zlinear Texture (class) a–d enumerated Texture (direction) a–i enumerated

For example, a large green fuzzy region of an image, perhaps an image ofa wool sweater, might be represented in this mapping by a regioncharacter string “rafambc”. Each coherent region of the currentlysegmented image is analysed in this way and allocated a region characterstring or “region word”. Each region word may then be embedded in amarkup language, for example XML to form an XML-like region-word filecontaining one tag per region, of the form:

-   -   <RW id=7 x=173 y=55>rafambc</RW>        where RW indicates region word, id is an identifier of the image        (in this case image number 7), x and y represent the position of        the centre of the region (for example relative to an origin at        the bottom left corner of the image and in terms of the number        of pixels horizontally and vertically from the origin), and the        region character string or word is “rafambc”.

The analysis performed by the region analyser 3 is also supplied to oneor more neural networks indicated generally at 6. At least some of theanalysed region data are used as inputs to the or each neural network,which is trained to recognise a common substance. For example,respective neural networks may be trained to recognise common substancessuch as sky, skin, grass, trees, water, etc. In this context, the term“substance” is not limited to a single material but encompasses anyregion comprising a visually distinguishable composite of one or morematerial types or basic substances where there may be a significantwithin-composite variation in visual appearance or parametricdescription returned by a segmentation routine.

If the neural network or one of the neural networks responds beyond apredetermined threshold, a semantic region-word, such as “sky” or “skin”corresponding to the above examples, is generated for the regioncurrently be analysed. These region-words are descriptive of the contentof the region and may be natural language words as in this example. Whenthe neural network 6 or one of the neural networks 6 responds in thisway, the resulting semantic region-word may be used in place of or inaddition to the region character string provided by the assembler 5.

The string assembler 5 and the neural network or networks 6 produce alist of region character strings or words which represent the visualcontent of each image in the image library 1. The list of characterstrings is supplied to a text indexer 7 which indexes the characterstrings, for example using standard text-indexing techniques such asthose used in word search engines, examples of which are AltaVista(http://www.altavista.com) and Google (http://www.google.com). Othersuitable techniques are disclosed in S. E. Robertson and K. SparckJones, “Simple, proven approaches to text retrieval”, Technical Report356, Cambridge University Computing Laboratory, 1997 and H. Turtle andW. B. Croft, “Evaluation of an inference network-based retrieval model”,ACM Transactions in Information Systems, Vol. 9, No. 3, pp 187–221,1991, the contents of which are incorporated herein by reference. Thetext indexer 7 thus forms an index 8 for all of the images in thelibrary 1 and the index can be searched, for example using standard textinformation retrieval techniques as described hereinafter, on the basisof a target image analysed using the same techniques or on the basis ofcharacter strings entered in any desired way. These possibilities areillustrated in FIG. 2.

When the images of the library 1 have been classified, they may bearranged in accordance with the region character strings associated withthem. Those images whose region character strings match most closely maybe arranged, at least notionaly, adjacent each other. For example, theimages may be reordered in accordance with the associated regioncharacter strings so that images with similar content are adjacent eachother. Thus, images which are visually similar to each other arearranged adjacent each other.

The image retrieval technique illustrated in FIG. 2 makes use of severalof the components shown in FIG. 1 and referred to by the same referencenumerals. Thus, when it is desired to find images having a similarcontent to a target image 10, the target image is supplied to the imagesegmenter 2 which segments the image as hereinbefore described. Theregion analyser 3, the property comparator 4, the string assembler 5 andthe neural network or networks 6 operate on the segmented target imageas described hereinbefore so that each of the coherent regions segmentedby the segmenter 2 is allocated a character string representing itsvisual content. The character string may be that allocated by theproperty comparator 4 and the string assembler 5, a semantic region-wordgenerated by one of the neural networks 6 or both of these.

Alternatively or additionally, character strings may be entered by meansof an input device 13 for forming the basis of an alternative oradditional search. For example, the region-words “sun”. “sky”, and“sand” may be entered manually in order to search for and retrieveimages of beach scenes.

The property comparator 4 supplies the characters for forming the regioncharacter strings to the string assembler 5 via a modifier 11. Themodifier 11 passes the characters allocated by the property comparator 4to the string assembler 5 and, in addition, selectively modifies some ofthe characters in accordance with predetermined rules so as to extendthe field of search for similar images. For example, some properties ofa region may be varied in order to extend the field of search to imageshaving, for example, different scales, where it is desired to retrieveimages having a similar content but in which the features of the imageare larger or smaller than in the target image, a different colour, etc.Thus, the modifier 11 may generate additional character strings in whichthe character representing, for example, the region size is changed torepresent the next bigger and/or next smaller band of possible sizes.Similar variations or “smudging” may be desirable with other regionproperties, such as colour as mentioned above. The string assembler 5assembles the unmodified characters into a region character string andalso assembles the modified characters into further region characterstrings for use in searching and retrieval.

All of the region character strings are supplied to a search engine 12which searches the index 8 for matches with the region character stringsformed from the target image 10. The searching strategy comprises or isbased on conventional text retrieval techniques and the search engine 12determines the identities of images in the image library 1 where atleast one of the target image region character strings matches a regioncharacter string of the image in the library. The image and details ofthe matches which have been found are supplied to a prioritiser 14 whichorders the images, if more than one has been found, from the imagelibrary in accordance with the likelihood of a match with the targetimage. For example, the prioritiser 14 may ascribe to the retrievedimage a priority based on the number of target image region characterstrings which match with region character strings of the retrievedimage. The retrieved images may then be supplied in order of priority toan output device 15, which may, for example, comprise a display or aprinter.

Prioritisation performed by the prioritiser 14 may be based on otherfactors or combinations of factors. For example, higher priority may begiven to retrieved images where regions whose characters strings matchwith target image region character strings are located in the samerelative positions in the target image and in the retrieved image.

In order to illustrate the operation of the above-described imageclassification and retrieval systems, a simplified specific example willbe described. FIG. 3 shows an image in the form of a photograph of redand green grapes for classification in accordance with the systemillustrated in FIG. 1. The original colour image has been converted to amonochrome image to meet the requirements of patent drawings.

The image in computer-readable form is supplied from the image library 1to the image segmenter 2, which segments the image into coherentregions. The boundaries of the coherent regions are illustrated in FIG.4 in black. In FIG. 4, the segmented regions are filled in in theaverage grey level of the corresponding regions in FIG. 3; for colourimages, the average or mean colour of the region is used for filling in.In the present highly simplified example, the region properties whichwill be considered are brightness, size and texture. However, inpractice, further properties of the coherent regions would be analysedand classified as mentioned hereinbefore. The numeric ranges of thesethree properties are as follows:

Property Numeric range Brightness 0 to 255 Size (pixels) 1 to 1000000Texture (smoothness) 0 to 100

The number of bands for each of these properties and the charactermapping in the present case is as follows:

Property Number of bands Character Mapping Brightness 5 [0 to 50] = “a”[51 to 101] = “b” [102 to 152] = “c” [153 to 203] = “d” [204 to 255] =“e” Size 3 [1 to 10000] = “a” [10001 to 100000] = “b” [100001 to1000000] = “c” Texture 10 [0 to 10] = “a” [11 to 20] = “b” . . . [91 to100] = “j”

In this example, the brightness and texture mappings are substantiallylinear whereas the size mapping is substantially logarithmic. However,other mappings may be used.

Examples of coherent regions in FIG. 4 are indicated by A and B. Theregion analyser 3 analyses the properties of the region B and providesvalues of 200 for the brightness, 50000 pixels for the size and atexture value of 5. The property comparator 4 compares these values withthe bands in the above table and ascribes the characters “d”, “b” and“a” to the brightness, size and texture, respectively. The stringassembler 5 assembles these to form a region character string for theregion B of “dba”. In this case, it is assumed that no neural network istrained to recognise grapes. The text indexer 7 creates an index entryin the index 8 for the region B based on the region character string,for example using the XML—like format described hereinbefore. This isthen repeated for all of the other coherent regions illustrated in FIG.4.

In this specific example, image retrieval is then performed on the basisof the target image shown in FIG. 5, which is a photograph of greengrapes, cheese and biscuits. This image is supplied to the imagesegmenter 2 of the retrieval system shown in FIG. 2 which segments thetarget image as illustrated in FIG. 6. Again in FIG. 6, the regions arefilled in with the average grey level of the corresponding regions ofthe target image in FIG. 5. The region analyser 3 analyses theproperties of each region, for example starting with the region C inFIG. 6, and determines that this region has a brightness of 180, a sizeof 30000 and a texture of 10. The property comparator 4 compares thesevalues with the bands in the above table and determines that thebrightness corresponds to the character “d”, the size corresponds to thecharacter “b” and the texture corresponds to the character “a”. Thesecharacters are supplied via the modifier 11 to the string assembler 5which creates the region character string “dba” and supplies this to thesearch engine 12.

The search engine 12 may respond to each region character string as itis created or may wait until all of the region character strings for thetarget image 5 have been created. In either case, the index 8 is thensearched for matches and, in this case, finds a match between a regioncharacter string for the image of FIG. 3 and the region character stringfor the image of FIG. 5. The image of FIG. 5 is retrieved from the imagelibrary and is supplied to the output device 15. If matches with otherimages are found, the resulting set of images may be prioritised orranked by the prioritiser 14, for example according to how many othermatching region character strings were found or according to therelative location of matching regions (or any other suitable criteria).The prioritiser 14 orders the supply of images from the library 1 to theoutput device 15 so as to try to ensure that the most similar imagesappear at the top of a list of retrieved images or are displayed inorder of ranking or priority by the output device 15.

FIG. 7 illustrates another target image comprising a photograph of greengrapes. When this target image is supplied to the system shown in FIG.2, it is segmented as illustrated in FIG. 8 and the region analyser 3determines that the coherent region E has a brightness of 200, a size of9000 and a texture of 3. The property comparator 4 determines that thecorresponding characters are “d”, “a” and “a”. The string assembler 5forms this into a region character string “daa” and supplies this to thesearch engine 12. A search based on this region character string wouldnot, therefore, find a match with the region B of the image shown inFIG. 3. However, the modifier 11 is arranged, in this example, to varythe character representing the size in order to attempt to locatematches with similar images of different scales. In particular, in thepresent case, the modifier 11 also supplies the character b representingthe size to the string assembler 5 which additionally supplies theregion character string “dba” to the search engine 12. The resultingsearch thus retrieves the image of FIG. 3 as a match to the target imageof FIG. 7.

FIG. 9 illustrates a computer-based apparatus for embodying the imageclassification and retrieval systems of FIGS. 1 and 2. The apparatuscomprises a central processing unit (CPU) connected to the input device13 and the output device 15. In the case where the image library 1 isremote, for instance accessible via the internet, the CPU 20 receivesthe image data via an input/output interface 21 of any suitable type.

A program for controlling the operation of the CPU to perform the imageclassification and retrieval as described hereinbefore is contained in asuitable medium, such as a read only memory ROM 22 and/or a CD-ROM 23.Where the image library is provided locally, it may likewise becontained in a CD-ROM. The CPU 20 is provided with “working memory”illustrated as a random access memory RAM 24.

The index formed by the image classification system may be stored in anysuitable form. For example, this may be stored in the RAM 24 providedthis is of the non-volatile type. As an alternative or additionally, theindex may be supplied to a CD writer (CDR) 25 so that the index isstored in a CD-ROM.

During image retrieval, the target image may be supplied via theinterface 21. Other region character stings may be entered by means ofthe input device 13. In the case of a remote image library 1, images maybe retrieved via the input/output interface 21. In the case of a locallystored image library, the images may be retrieved from the CD-ROM 23.Retrieved images may then be supplied to the output device, for examplefor display on a display screen or for printing to provide a hardcopy.

1. A computerized method of classifying an image, comprising the stepsof segmenting the image into a plurality of regions and, for each of atleast one of the regions: quantifying each of a plurality of visualproperties of the region on a numeric scale for the property; comparingeach quantified property with a plurality of bands of the numeric scalefor the property, each band being associated with a computer-readablecharacter; and arranging in a predetermined order the charactersassociated with the bands in which the quantified properties fall toform a region character string.
 2. A method as claimed in claim 1, inwhich the segmenting step comprises segmenting the image into aplurality of coherent regions.
 3. A method as claimed in claim 1, inwhich the visual properties include area.
 4. A method as claimed inclaim 3, in which the numeric scale for area is logarithmic.
 5. A methodas claimed in claim 1, in which the visual properties include at leastone colour property.
 6. A method as claimed in claim 5, in which thenumeric scale for the colour property is linear.
 7. A method as claimedin any one of the preceding claims, in which the visual propertiesinclude brightness.
 8. A method as claimed in claim 7, in which thenumeric scale for brightness is linear.
 9. A method as claimed in claim1, in which the visual properties include shape.
 10. A method as claimedin claim 1, in which the visual properties include texture.
 11. A methodas claimed in claim 1, in which each region character string includestwo-dimensional coordinates representing the position of thecorresponding region in the image.
 12. A method as claimed in claim 1,comprising supplying at least one of the quantified properties to atleast one neural network which is trained to recognise at least onesubstance and forming, in response to recognition by the neural network,another region character string.
 13. A method as claimed in claim 12, inwhich the other region character string is descriptive of the at leastone substance.
 14. A method as claimed in claim 13, in which the otherregion character string is a semantic word.
 15. A method as claimed inclaim 1, comprising associating the region character strings with theimage.
 16. A method as claimed in claim 1, in which the region characterstring is embedded in a markup language.
 17. A method of classifying aplurality of images, comprising classifying each of the images by amethod as claimed in claim
 1. 18. A method as claimed in claim 17,comprising forming an index of the images by the region characterstrings.
 19. An index formed by a method as claimed in claim
 18. 20. Amethod of image retrieval, comprising generating at least one regioncharacter string and comparing the at least one region character stringwith the character strings formed by a method as claimed in claim 17.21. A method as claimed in claim 20, in which the comparison isperformed by a text retrieval system.
 22. A method of organising aplurality of images, comprising classifying the images by a method asclaimed in claim 17 and arranging the images in accordance with theregion character strings associated therewith.
 23. A method as claimedin claim 22, in which images whose region character strings match mostclosely are arranged adjacent each other.
 24. An apparatus comprising acomputer programmed via a program embodied in a computer readable mediumfor performing a method as claimed in claim
 1. 25. A program embodied ina computer readable medium for controlling a computer of an apparatus asclaimed in claim
 24. 26. An apparatus for classifying an image,comprising: computer means for segmenting the image into a plurality ofregions; computer means for quantifying each of a plurality of visualproperties of at least one of the regions on a numeric scale for theproperty; computer means for comparing each quantified property with aplurality of bands of the numeric scale for the property, each bandbeing associated with a computer-readable character; and computer meansfor arranging in a predetermined order the characters associated withthe bands in which the quantified properties fall to form a regioncharacter string.